VENDOR SELECTION OF DECISION ENGINE PLATFORM AT BANK PINTAR USING AHP MODEL

• Main Author: BERGAS (NIM 29316083), YERRIKHO
• Format: Theses
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/31731
• Institution: Institut Teknologi Bandung

BANK PINTAR as one of the emerging bank in Indonesia get pressure from competition with fintech. The growths of p2p lending business model of fintech increasingly burdensome the bank to compete. Some of the bank in Indonesia right now very slow progress while digitalizing the lending process. BANK PINTAR seeks alternative to form a new business model which will automate lending process. Therefore, the Bank created a large project called core lending system and the main part of the core lending system is the decision engine platform. <br /> <br /> <br /> The objective of this research is to develop a decision making support model using Analytical Hierarchy Process (AHP) to select the best vendor for Decision Engine Platform. The alternative vendor will align with BANK PINTAR for providing single decisioning engine framework omni channel, standardized policy framework, better risk management by control customer level and customer satisfaction, and at the end will be end to end integrated automation tools. AHP chosen as framework process because the framework decompose complex problem into simpler forms to then synthesize the various factors involved in the decision making problem. <br /> <br /> <br /> All data conducted by qualitative and quantitative method. The primary data collected from interview and discussion with decision makers who acted as project team, and also questionnaire. Primary data also obtained by interviewing six respondents’ decision makers. Secondary data collected from company research document and another data coming from response documents from alternatives vendors. <br /> <br /> <br /> From the results of data processing found that the criteria and sub criteria with the most important weighting in this project is functional capability for criteria and decision engine for origination process (functional), integration capability (technical), reputation, people expertise, and team capability (proof of concept) for sub criteria. After we get the results for the criteria and sub criteria, then we do weighting the alternative for vendors who will be appointed for this project. And the ranking results for four candidates are EN, FO, PR, and CF. The research shows that AHP model can also be used for decision support system specifically related with vendor selection that has complex criteria and large number of respondents from various units and background.